"How to execute EKS (Elastic Kubernetes Service) commands" matching MCP servers:

An MCP server for tracking and managing AI command usage history using a PostgreSQL database. It enables users to log, search, and view statistics for various AI-related commands and their execution contexts.

A minimal MCP server with get_weather and create_ticket tools, used for testing MCP servers across protocol, unit, eval, transport, and auth layers.

Small production-oriented MCP server for diagnosing incidents from Elasticsearch logs with unknown schema. It provides tools for log discovery, retrieval, and issue diagnosis.

Connects to Elasticsearch clusters through the Model Context Protocol, enabling natural language querying and management of Elasticsearch data. Provides tools to search indices, list available indices, and retrieve index mappings.

Enables querying Amazon EKS cluster metrics from the Kubernetes API server, with filtering by metric name and labels, supporting both exact and regex patterns.

An MCP server that adds a safety layer with eight independent checks and audit logging between a language model and a broker order, supporting both on-disk simulation and live MetaTrader 5 terminals.

Enables creating and managing AWS EKS clusters via Terraform, including VPC, node groups, add-ons, and kubectl configuration.

Enterprise-grade MCP server for Kubernetes operations, supporting local kubeconfig and AWS EKS authentication, with read, write, and destructive operations protected by multi-layer safety guards.

A Python MCP server that provides AI agents with a controlled, read-first interface to Elasticsearch, Kibana Security, Fleet, and Elastic Defend.

Enables AI assistants to analyze Amazon EKS node logs collected by the EKS Log Collector script. It provides tools and troubleshooting workflows for diagnosing issues with VPC CNI, kubelet, DNS, and general node connectivity.

Enables inspecting and managing Kubernetes clusters through MCP, including listing deployments and pods, retrieving logs and events, and scaling or restarting deployments.

An MCP server for generating Kubernetes manifests (deployments, services, configmaps, secrets, ingresses, namespaces) and performing kubectl operations like apply, delete, get, describe, logs, and exec.

A lightweight MCP server that enables natural language interaction with Kubernetes clusters, allowing management of pods, deployments, namespaces, and cluster resources through conversational queries or API endpoints.

An MCP server that enables AI assistants to interact with Kubernetes clusters by translating natural language into kubectl and Helm operations. It allows users to query, manage, and diagnose Kubernetes resources and cluster states through a seamless integration.

Enables secure Kubernetes operations with configurable security modes, supporting core Kubernetes resources, Istio service mesh, and Helm chart management through natural language.

Enables comprehensive Kubernetes cluster management through kubectl operations and Helm chart management. Supports resource operations, logging, scaling, rollouts, and diagnostics with multiple transport modes and security features.

Provides access to Kubernetes troubleshooting runbooks, enabling search, discovery, and retrieval of guides for common issues like pod crashes, node problems, and network failures.

Enables safe, read-only interaction with Kubernetes clusters, allowing users to list resources and fetch logs without any create/update/delete operations.

Enables diagnosing Kubernetes clusters without creating or changing cluster objects, with tools to inspect health, workloads, events, storage, RBAC, custom resources, logs, and configuration.

Provides tools to communicate with a service dependency API, enabling users to query and interact with service relationship data and dependency mappings.